Modified algor ithm for searching regularities in large dimensional data based on genetic optimization

A method of searching for patterns in sequences of events is proposed for detecting hidden patterns in largedimensional data when performing information retrieval tasks, based on the theory of sequential patterns. Searching for sequential patterns is a complex computational task whose goal is to retrieve all frequent sequences representing potential relationships within elements from a transactional database of sequences of search activity events with a given minimum support. To increase the computational efficiency of the method, a modified algorithm for generating sequential patterns has been developed, at the first stage of which AprioriAll is performed, which forms frequent candidate sequences of all possible lengths, and at the second stage, a genetic algorithm for optimizing the input parameters of the feature space of the generated set to search for maximum patterns.